Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption. However, breeding programs are challenging due to long turnover times, high-dimensional decision spaces, long-term objectives, and the need to adapt to rapid climate change. This paper introduces the use of Reinforcement Learning (RL) to optimize simulated crop breeding programs. RL agents are trained to make optimal crop selection and cross-breeding decisions based on genetic information. To benchmark RL-based breeding algorithms, we introduce a suite of Gym environments. The study demonstrates the superiority of RL techniques over standard practices in terms of genetic gain when simulated in silico using real-world genomic maize data.
@article{arxiv.2406.03932,
title = {Breeding Programs Optimization with Reinforcement Learning},
author = {Omar G. Younis and Luca Corinzia and Ioannis N. Athanasiadis and Andreas Krause and Joachim M. Buhmann and Matteo Turchetta},
journal= {arXiv preprint arXiv:2406.03932},
year = {2024}
}
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NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning